적합한 경우
Teams already deep on LangChain / LangGraph that want traces, scoring, datasets, and replay in one loop—especially to ship a change and run 200 regressions in one click.
덜 맞는 경우
Minimal stacks that call APIs directly, strict OSS/air-gapped requirements, or teams that don’t use the LangChain ecosystem.
비교 시 참고
Compare with Langfuse / Braintrust / Arize Phoenix on custom scorer depth, dataset management, and whether offline/online share one store.
점검 체크리스트
- Verify project-level permissions and PII redaction
- Model trace sampling vs cost at your volume
- Build a 50+ example regression set before deciding
- Review self-hosting/enterprise plan requirements
검색 Q&A
LangSmith vs Langfuse—how to choose?
LangSmith is deepest if you already build with LangChain/LangGraph; Langfuse is open-source and self-hostable, which wins when OSS/data-locality matters. Features overlap—wire real traffic into both for a week before committing.
What metrics should an LLM eval cover?
Business Q&A needs groundedness + hallucination sampling + human scores; structured extraction needs field-level F1; agentic tasks add success rate and step count. Always pair these with P95 latency and per-call cost.
활용 상황
위 소개로 이 도구가 적합한지 가늠할 수 있습니다. 비슷한 도구가 많다면 사용 빈도, 예산, 데이터 프라이버시를 먼저 정리하고 고르세요.